Data Scientist, NLP & Trading Strategies (Quantitative): Binance

As a Data Scientist focusing on Quantitative Trading NLP at Binance, you'll be instrumental in developing and refining algorithmic trading strategies by leveraging Natural Language Understanding (NLU) techniques. This involves analyzing financial news, social media, and other text streams to extract predictive signals like sentiment, intent, and named entities. You will apply advanced mathematics and machine learning to build, backtest, and optimize models to maximize returns while managing risk in the fast-paced crypto market.

Responsibilities
Research and develop quantitative trading strategies using NLU methods (sentiment analysis, intent recognition, named-entity extraction) on text sources.

Design and build machine-learning models to uncover predictive trading signals and perform exploratory data analysis on large, complex datasets.

Apply mathematical techniques (probability, statistics, time-series analysis) to refine and strengthen trading models.

Rigorously backtest strategies against historical data and iteratively optimize models to boost performance and curb risk.

Requirements
A Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Financial Engineering, or a related discipline.

Strong mathematical foundation in probability, statistics, linear algebra, and time-series analysis.

Familiarity with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.

Solid grasp of NLU techniques and experience with NLP libraries (SpaCy, NLTK, Transformers).

Proficiency in Python or R.

A passion for exploring the undefined problem space in the fast-changing crypto world.